A case-based architecture for temporal abstraction configuration and processing

In this work we propose a case-based architecture tackling the problem of configuring and processing temporal abstractions (trends and qualitative states) produced from raw time series data. The parameter configuration is a critical problem in many temporal abstraction processes; in several application domains (especially in medical ones), contextual knowledge plays a fundamental role in the time series interpretation. Since defining the right configuration for each possible contextual situation may be impractical, we propose to adopt a case-based approach, where the suitable configuration can be obtained by looking at the most similar already configured case, with respect to the current situation. Configured cases are indexed by means of contextual information. The obtained configuration can then be used as input to a temporal abstraction module, providing a set of qualitative states, trends and suitable combination of both as a result. Cases can then be exploited in the processing of such results as well, by providing an evaluation of the whole abstraction processing, possibly leading to the revision of the case base. The approach is illustrated by means of an example taken from a medical application, concerning the monitoring and evaluation of patients undergoing hemodialysis treatment.